A Study on Multi-objective Vehicle Rounting Problem considering Customer Satisfaction with Due-time (The Creation of Pareto Optimal Solutions by Hybrid Genetic Algorithm)

In this paper, we proposed a hybrid genetic algorithm (Hybrid GA) approach to a multi, objective vehicle routing problem (MVRP). The objective functions considered in this MVRP are (1). to minimize the number of vehicles used, (2). to minimize the total traveling distance for vehicles, (3). to minimize the total waiting time for vehicles, and (4). to maximize the grade of customer satisfaction with due-time. With respect to customer satisfaction with due-time, we used the concept of fuzzy due-time because it can describe customers' preference with service time better than crisped expression of satisfaction with 0 and 1. To handle such multi-objectivity, a set of Pareto optimal solutions are searched by Hybrid GA. Among Pareto optimal solutions, we furthermore targeted at compromise solutions whose objective functions take almost intermediate values each, in order to produce realistic routing plans for vehicles. In the proposed algorithm, a local search procedure is applied to each solution at each generation for efficient search of solutions. The computational results show that the proposed algorithm is efficient for solving MVRP.